One technique using HRTF transfer functions is binaural synthesis. Binaural synthesis is based on the use of so-called “binaural” filters, which reproduce the acoustic transfer functions between the sound source or sources and the ears of the listener. These filters serve to simulate hearing location indices which allow a listener to locate the sound in a real listening situation.
The techniques using binaural synthesis are therefore based on a pair of binaural signals which feed a restitution system. These two binaural signals can be obtained by processing the signal, by filtering a monophonic signal with the binaural filters which reproduce the acoustic propagation properties between the source placed in a given position and the two ears of a listener.
Such binaural synthesis can be used for different restitutions such as for example restitution using a headset with two earphones, or two loud speakers. The objective is the reconstruction of a sound field at the level of a listener's ears which is practically identical to that which would be induced by the real sources in space.
Binaural filters take account of all of the acoustic phenomena which modify the acoustic wave on its path between the source and the listener's ears. These phenomena include in particular the diffraction by the head and the reflections on the auricle and the upper part of the torso.
These acoustic phenomena vary according to the position of the sound source with respect to the listener and these variations make it possible for the listener to locate the source in space. In fact, these variations determine a kind of acoustic encoding of the position of the source. The hearing system of an individual system knows, by learning, how to interpret this encoding in order to locate the sound sources. However, the acoustic phenomena of diffraction/reflection depend greatly on the morphology of the individual. Quality binaural synthesis is therefore based on binaural filters which reproduce as best as possible the acoustic encoding that the listener's body produces naturally, taking account of the individual distinctiveness of his or her morphology. When these conditions are not complied with, a degradation of the binaural rendering performance is observed, which results, in particular, in an intracranial perception of the sources and confusions between the front and back locations.
Thus, these filters represent the acoustic or HRTF transfer functions which model the transformations, generated by the listener's torso, head and auricle, of the signal originating from a sound source.
Each sound source position is associated with a pair of HRTF functions, one for each ear. Moreover, these HRTF transfer functions bear the acoustic imprint of the morphology of the individual upon whom they were measured.
Conventionally, the HRTF transfer functions are obtained during a measurement phase. Initially a selection of directions which more or less finely covers the whole of the space surrounding the listener is fixed. The left and right HRTFs are measured for each direction using microphones inserted in the entrance of the listener's auditory canal. In general, a sphere centred on the listener is thus defined.
For a measurement of good quality, the measurement must be carried out in an anechoic chamber, or “dead room”, such that only the acoustic reflections and phenomena related to the listener are taken into account. Finally, if N directions are measured, there is obtained, for a given listener, a database of 2N HRTF transfer functions representing, for each ear, each of the positions of the sources.
These techniques therefore require making measurements on the listener. The duration of this measuring operation is very significant because it is necessary to measure a large number of directions.
It is therefore desirable to reduce the number of measurements specific to a listener whilst retaining good modelling quality.
Statistical learning techniques address this problem. This is the case of the technique described in the patent document FR 0500218. However, statistical learning systems are difficult to adjust and to improve because the link between the parameters of the learning algorithm and their impact on the HRFT transfer functions is difficult to comprehend.